Combining entity co-occurrence information and sentence semantic features for relation extraction

Abstract

Relation extraction is one of the most important tasks in information extraction and a key step in knowledge graph construction. The existing relation extraction approaches mostly try to capture semantic features for entity pairs at the sentence level, which might ignore the global context information of the entities in the entire corpus. In this paper, we propose a novel neural network model for relation extraction, named CNSSNN, which combines the information of entity co-occurrences with sentences semantic features. In this model, we first build an entity co-occurrence network from the corpus. Then, we introduce a network-level attention mechanism to capture network environmental information selectively and generate the corpus-level global context features for the entities. At the same time, we employ a bi-directional gated recurrent unit (bi-GRU) network to extract sentence-level semantic features for entity pairs. Finally, we combine the corpus-level features and the sentence-level features to classify relations. The experimental results, over a manually labeled dataset, show that our approach consistently outperforms other existing approaches in terms of both precision and recall.